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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Evaluation of the Biolog MicroStation system for yeast identification

One hundred and fifty-nine isolates representing 16 genera and 53 species of yeasts were processed with the Biolog MicroStation System for yeast identification. Thirteen genera and 38 species were included in the Biolog database. For these 129 isolates, correct identifications to the species level were 13.2, 39.5 and 48.8% after 24, 48 and 72 hours incubation at 30 degrees C, respectively. Three genera and 15 species which were not included in the Biolog database were also tested. Of the 30 isolates studied, 16.7, 53.3 and 56.7% of the isolates were given incorrect names from the system's database after 24,48 and 72 h incubation at 30 degrees C, respectively. The remaining isolates of this group were not identified.

NASA Program Environmental Health↗
NASA NTRS1996

Evaluation of Automated Yeast Identification System

One hundred and nine teleomorphic and anamorphic yeast isolates representing approximately 30 taxa were used to evaluate the accuracy of the Biolog yeast identification system. Isolates derived from nomenclatural types, environmental, and clinica isolates of known identity were tested in the Biolog system. Of the isolates tested, 81 were in the Biolog database. The system correctly identified 40, incorrectly identified 29, and was unable to identify 12. Of the 28 isolates not in the database, 18 were given names, whereas 10 were not. The Biolog yeast identification system is inadequate for the identification of yeasts originating from the environment during space program activities.

McGinnis, M. R.↗
DOE OSTI2024

ToF-SIMS spectral analysis of Shewanella oneidensis MR-1 biofilms

Analysis of bacterial biofilms is particularly challenging and important with diverse applications from systems biology to biotechnology. Among the variety of techniques that have been applied, time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many powerful features in studying the surface characteristics of biofilms. ToF-SIMS offers high spatial resolution, mass resolution, and mass accuracy, which permit surface sensitive analysis of biofilm components. Thus, ToF-SIMS provides a powerful solution to addressing the challenge of bacterial biofilm analysis. This dataset covers ToF-SIMS analysis of Shewanella oneidensis MR-1 isolated from freshwater lake sediment in New York state. The MR-1 strain is known to have metal and sulfur reducing properties and it can be used for bioremediation and wastewater treatment. There is a current need to identify small molecules and fragments produced from bacterial biofilms, especially those from extracellular polymeric substance (EPS). Static ToF-SIMS spectra of MR-1 were obtained using an IONTOF TOF.SIMS V instrument equipped with a 25 keV Bi$^+_3$ metal ion gun. Identified molecules and molecular fragments are compared against known biological databases and the reported peaks have at least 65 ppm mass accuracy. These molecules range from lipids, fatty acids, flavonoids, and quinolones to other naturally occurring organic compounds. It is anticipated that the mass spectral identification of key peaks will assist detection of metabolites, EPS molecules like polysaccharides, and biologically relevant small organic molecules using ToF-SIMS in future surface and interface research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

ToF-SIMS spectral data analysis of Paenibacillus sp. 300A biofilms and planktonic cells

Analysis of bacterial biofilms is particularly challenging and important with diverse applications from systems biology to biotechnology. Among the variety of techniques that have been applied, time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many promising features in studying the surface characteristics of biofilms. ToF-SIMS offers high spatial resolution and high mass accuracy, which permit surface sensitive analysis of biofilm components. Thus, ToF-SIMS provides a powerful solution to addressing the challenge of bacterial biofilm analysis. This dataset covers ToF-SIMS analysis of Paenibacillus sp. 300A (300A) isolated from the Hanford site in Richland, WA. The strain is known to have metal and sulfur reducing properties and can be used for bioremediation, wastewater treatment, bioengineering and technology development. There is a current need to identify small molecules and fragments produced from bacterial biofilms. Static ToF-SIMS spectra of 300A were obtained using an IONTOF TOF-SIMS V instrument equipped with a 25 keV Bi 3 + metal ion gun. Identified molecules and molecular fragments are compared against known biological databases and the reported peaks have at least 65 ppm mass accuracy. These molecules range from lipids and fatty acids to flavonoids, quinolones, and other naturally occurring organic compounds. It is anticipated that the spectral identification of key peaks will assist detection of metabolites, extracellular polymeric substance molecules like polysaccharides, and biologically relevant small molecules using ToF-SIMS in future surface and interface research of bacterial biofilms.

Biofilms↗
DOE OSTI2022

Graph-based featurization methods for classifying small molecule compounds

For over a decade, drug-induced liver injury (DILI) has posed significant drawbacks in the synthesis and development of drugs and remains a consequential concern. With finite success within the existing preclinical models, DILI is one of the main causes of drug withdrawal or termination from the market. Particularly, this withdrawal occurs during the late stages of drug development (Kullak-Ublick, 2017). Since DILI is difficult to diagnose and treat, it has become an obstacle in the drug production market that in turn affects clinicians, pharmaceutical companies, and consumers. We propose a method for learning features of DILI-positive drugs based on the graphical relationships and patterns they possess within a network of biological databases. We also train various statistical and machine learning models on these learned features in order to classify the drugs as DILI-positive or negative. Our methods include Random Forest, Neural networks, and logistic regression classification. We utilize labeled DILI-positive and DILI-negative datasets, which were developed by the FDA and the National center for toxicological research, as well as additional literature datasets (Thakkar, 2020) in order to validate our results and assess our featurization and model accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Pickaxe: a Python library for the prediction of novel metabolic reactions

Abstract Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

DiMER

SAND2025-04145O DiMER is a Python based tool that helps researchers understand the functions of genes by searching through multiple biological databases. It takes user-provided data and scans various databases to find the best matches for gene functions, generating a clear summary of results. DiMER identifies the most relevant functional annotations and improves upon previous annotations by replacing instances of "unknown protein function" with more accurate descriptions. DiMER requires minimal setup. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Mageeney, Catherine [Sandia National Lab. (SNL-CA)↗
DOE OSTI2020

53BP1 Repair Kinetics for Prediction of In Vivo Radiation Susceptibility in 15 Mouse Strains

We present a novel mathematical formalism to predict the kinetics of DNA damage repair after exposure to both low- and high-LET radiation (X rays; 350 MeV/n 40 Ar; 600 MeV/n 56 Fe). Our method is based on monitoring DNA damage repair protein 53BP1 that forms radiation-induced foci (RIF) at locations of DNA double-strand breaks (DSB) in the nucleus and comparing its expression in primary skin fibroblasts isolated from 15 mice strains. We previously reported strong evidence for clustering of nearby DSB into single repair units as opposed to the classic "contact-first" model where DSB are considered immobile. Here we apply this clustering model to evaluate the number of remaining RIF over time. We also show that the newly introduced kinetic metrics can be used as surrogate biomarkers for in vivo radiation toxicity, with potential applications in radiotherapy and human space exploration. In particular, we observed an association between the characteristic time constant of RIF repair measured in vitro and survival levels of immune cells collected from irradiated mice. Moreover, the speed of DNA damage repair correlated not only with radiation-induced cellular survival in vivo, but also with spontaneous cancer incidence data collected from the Mouse Tumor Biology database, suggesting a relationship between the efficiency of DSB repair after irradiation and cancer risk.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Defining criteria for broadly neutralizing HIV antibodies

Over the course of a few years, a small percentage of individuals with HIV-1 develop broadly neutralizing antibodies (bnAbs) capable of neutralizing diverse viruses. Although hundreds of antibodies with neutralizing activity against heterologous viruses have been referred to as bnAbs, there is no universally accepted numerical definition of a bnAb. Here, we will review important elements of HIV neutralizing antibodies and proposed definitions of bnAbs, as well as introduce a web-based tool, CAByN (Choose Antibodies by Neutralization), allowing users to identify antibodies meeting their numerical definitions of a bnAb from data in the Los Alamos HIV Databases CATNAP (Compile, Analyze and Tally NAb Panels) antibody neutralization database. Biological findings from use of CAByN are also presented here, including differential neutralizing activity for certain antibodies across viral clades, and identification of antibodies with suspected incomplete neutralization. Website address: http://hiv.lanl.gov/content/sequence/CABYN/CABYN.html.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Dose, LET, time and strain dependence of radiation-induced 53BP1 foci in 15 mouse strains ex vivo and associations to in vivo radiation susceptibility

We present a comparative analysis on the repair of radiation-induced DNA damage ex vivo in 15 strains of mice, including 5 inbred reference strains and 10 collaborative-cross strains, of both sexes. Non-immortalized primary skin fibroblasts derived from 76 mice were subjected to both low- and high-LET radiation (0.1, 1 and 4 Gy of X rays; 1.1 and 3 particles/100μm2 of 350 MeV/n 40Ar and 600 MeV/n 56Fe). Automated image quantification of 53BP1 radiation-induced foci (RIF) during the first 4-48 h post-irradiation was performed as a function of dose and LET. Similarly to what we had previously reported for immortalized human cell lines [1], we observed a saturation of RIF number with dose at 4h post-irradiation, with more RIF/Gy for lower LET (X rays and 40Ar) compared to 56Fe. However at later time points (24h and above), the trend was inverted with more RIF/Gy for higher LET. Our data suggest that multiple DSBs cluster into RIF: as the linear density of DSBs increases with LET, so does the probability of having more DSBs per RIF, which makes it more difficult for cells to fully resolve high-LET-induced RIF, explaining the hypersensitivity to high-LET radiation despite a low number of RIF. Taking into account the amount of clustering at a given dose and LET, but also the kinetics of DNA damage repair, we introduced a novel mathematical formalism to evaluate the number of remaining RIF over time. We showed that the newly introduced kinetic metrics can be used as surrogate biomarkers for in vivo radiation toxicity, with potential applications in radiotherapy and human space exploration. In particular, we observed an association between the repairable fraction of RIF measured in vitro and survival levels of immune cells collected from irradiated mice. Moreover, the speed of DNA damage repair correlated with spontaneous cancer incidence data collected from the Mouse Tumor Biology database, suggesting a relationship between the efficiency of DSB repair after irradiation and cancer risk. In addition to the efficacy of repair and persistent RIF levels, even the amount of spontaneous foci without irradiation was shown to be strain dependent, indicating that these phenotypes are at least partially driven by genetics, and supporting their potential as indicators of individual radiation sensitivity. [1] Neumaier, T., et al., PNAS, 2012 (8) 109:443

Radiation, DNA damage, repair kinetics↗
DOE OSTI2024

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs

Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

NASA biological and physical sciences databases: who’s the FAIRest of them all?

Conceptual models are a key part of the foundation of scientific study. Scientific data discovery and retrieval are often inaccurate and incomplete because these models are not sufficiently well-incorporated into data retrieval systems. Systems often don’t provide the necessary tools to those producing scientific data to fully and unambiguously annotate them and the result is consumers of the data cannot find them efficiently. The capability of data archives to provide these tools to link data to underlying conceptual models is one of dimensions of the recently developed “FAIR” principles (https://www.go-fair.org/fair-principles/ ), and is key to many automated processes being able to operate on these data, particularly analytics involving artificial intelligence. We used an open-source web service to measure the FAIR compliance of the three data archives operated by NASA for the biological and physical sciences: the Life Sciences Data Archive, the Physical Sciences Informatics database, and GeneLab. The service ingests references to data sets in these archives, and then executes domain-non-specific examinations of these data and metadata that test compliance to the FAIR principles. Of the 22 metrics tested, GeneLab passed 11 (50%), and PSI and LSDA each passed 7 (32%). These data were gathered using only one representative data set from each archive and we anticipate variability in results as we continue to apply these metrics to other data. A preliminary study of the failure traces for each metric suggests there is a wide range of effort and complexity in the enhancements required for each system to elevate FAIR compliance, and this is the subject of continued investigation. This information has been and will likely continue to be important information in planning these enhancements, with the goal of increased readiness of the data for automated processes.

database↗
DOE OSTI2021

Tripal, a community update after 10 years of supporting open source, standards-based genetic, genomic and breeding databases

Abstract Online, open access databases for biological knowledge serve as central repositories for research communities to store, find and analyze integrated, multi-disciplinary datasets. With increasing volumes, complexity and the need to integrate genomic, transcriptomic, metabolomic, proteomic, phenomic and environmental data, community databases face tremendous challenges in ongoing maintenance, expansion and upgrades. A common infrastructure framework using community standards shared by many databases can reduce development burden, provide interoperability, ensure use of common standards and support long-term sustainability. Tripal is a mature, open source platform built to meet this need. With ongoing improvement since its first release in 2009, Tripal provides full functionality for searching, browsing, loading and curating numerous types of data and is a primary technology powering at least 31 publicly available databases spanning plants, animals and human data, primarily storing genomics, genetics and breeding data. Tripal software development is managed by a shared, inclusive governance structure including both project management and advisory teams. Here, we report on the most important and innovative aspects of Tripal after 11 years development, including integration of diverse types of biological data, successful collaborative projects across member databases, and support for implementing FAIR principles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Predictive Synthesis

Current solid state synthesis intrinsically involves a multidimensional space which is challenging to parametrize and predict. The diversity of extended structures comes from the diversity of basic properties of elements of the Periodic system which may exhibit a variety of bonding modes. The fundamental challenges of prediction of the preparative outcome are further complicated by practical synthetic issues. Current development in computational and experimental methods calls for collaborative efforts to make solid state synthesis more predictable. Overall, this Perspective discusses several steps in this direction, including integration of predictions of synthetic conditions with new structure predictions, widespread in situ studies to obtain a panoramic view of the reaction mechanism, and the creation of a synthetic database to properly document all synthetic efforts, including the unsuccessful ones.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Identifying Abandoned Well Sites Using Database Records and Aeromagnetic Surveys

Oil and natural gas are primary sources of energy in the United States. Improved drilling and extracting techniques have led to a renewed interest in historic oil and gas fields, but limited records of legacy wells make new drilling efforts more difficult, as abandoned wells may provide conduits for liquids and gases to migrate into groundwater reservoirs or the atmosphere. Well finding using aeromagnetic surveys pinpoints the location of steel-cased wells, detecting both active and abandoned wells, including buried casings lacking aboveground markers. Here, we present six aeromagnetic surveys conducted in Pennsylvania and Wyoming as case studies, comparing the magnetic points to locations known in databases. In all study sites, more magnetic points were detected than recorded in databases. Based on differences between theoretical database well counts and the actual number of wells detected in surveys, we estimated the total number of wells in Pennsylvania to be 395 000–466 000 and 181 000–182 000 in Wyoming. Extrapolating to the national level, in this work, we estimate the average number of wells in the continental United States is 6.04 ± 19.97 million wells with 1.16 ± 3.84 million of those designated as abandoned wells, within the range of previous abandoned well count estimations. Although aeromagnetic surveys are limited to detecting steel-cased wells and do not differentiate sites based on well status, this study nevertheless demonstrates the utility of aeromagnetic surveys in well finding efforts across the country and shows limitations in database records of oil and natural gas wells.

54 ENVIRONMENTAL SCIENCES↗